Against the problem that compression algorithms for bag-of-features(BoF) ignore the spatial relationships of coded vectors

we propose a fusing algorithm of compression algorithms and spatial pyramid model in this paper. Meanwhile

we carried out a set of comparative experiments on several public image datasets. The experimental results show that compression algorithms are robust to visual word numbers and pooling methods of coded vectors. Otherwise

compression algorithms based on subspace methods have achieved best classification performances in the high-level feature space

and best accuracies and smallest time cost in multiple image datasets.